What does multivariate data look like?

Multiple variables but not a multivariate plot:

Source: https://www.cdc.gov/nchs/images/databriefs/201-250/db217_fig1.png

Multivariate categorical data

Frequency

• Bar charts
• Cleveland dot plots

Proportion / Association

• Mosaic plots

Change of State

• Alluvial diagrams

Two continuous variables

Two categorical variables

Two categorical variables

Two discrete variables

(same problem)

Stacked bar chart

Grouped bar chart

Grouped bar chart

Grouped bar chart w/ facets

Grouped bar chart w/ facets

Three categorical variables

Cleveland dot plot (two variables)

Cleveland dot plot (three variables)

Cleveland dot plot (three variables)

Cleveland dot plot (three variables)

Proportion / Association

Are older Americans more interested in local news than younger Americans?

We ask 34892 U.S. adults whether or not they follow local news “very closely”. 34.5% say yes.

Group sizes are:

Source: https://www.journalism.org/2019/08/14/methodology-local-news-demographics/

If older Americans are NOT more interested in local news, what would the breakdowns look like?

Who follows local news?

How can we graph this data?

Mosaic plot

Now let’s look at the actual data…

U.S. adults who closely follow local news

Chi Square Test of Independence

Null hypothesis: Age and tendency to follow local news are independent

Alternative hypothesis: Age and tendence to follow local news are NOT independent

We compare OBSERVED to EXPECTED:

localmat <- as.matrix(local[,2:3])
rownames(localmat) <- local$Age
X <- chisq.test(localmat, correct = FALSE)
X$observed

##       Followers Nonfollowers
## 18-29       428         2423
## 30-49      2791         7176
## 50-64      4242         6921
## 65+        4583         6328

X$expected

##       Followers Nonfollowers
## 18-29  984.1065     1866.893
## 30-49 3440.4032     6526.597
## 50-64 3853.2378     7309.762
## 65+   3766.2526     7144.747

X

## 
##  Pearson's Chi-squared test
## 
## data:  localmat
## X-squared = 997.48, df = 3, p-value < 0.00000000000000022

Compare

Creating mosaic plots

Mosaic plots (one variable)

Mosaic plots (one variable)

Mosaic plots (two variables)

Mosaic plots (two variables)

What if there were no relationship between the variables?

Mosaic plots (two variables)

What if there were a deterministic relationship between the variables?

Mosaic plots (three variables)

Mosaic plots (three variables, Observed vs. Expected)

Best practices

Mosaic pairs plot

Mosaic pairs plot

Occupational Mobility data (UK only)

vcdExtra::Yamaguchi87

UK Mosaic

UK Mosaic (no mobility hypothetical)

Mosaic Mobility Plot

Mosaic Mobility Plot

Plot names

mosaic plot = any filled rectangular plot (no white space) with consistent numbers of rows and columns, in which the area of each small rectangle is proportional to the frequency count for a unique combination of levels of the categorical variables displayed

Plot names: mosaic plot vs. tree map

mosaic plot = filled rectangular plot with consistent number of rows and columns, where each small rectangle represents a unique combination of levels of factors of the variables displayed

treemap = filled rectangular plot representing hierarchical data (fill color does not necessarily represent frequency count)

Mosaic plots: spine plot

spine plot = mosaic plot with straight, parallel cuts in one dimension (“spines”) and only one variable cutting in the other direction

Mosaic plots

MASS::housing

Same bin width mosaic plots

= relative frequency stacked bar charts

p. 145

Categorical data formats

• cases
• counts (tidy data with Freq column)
• contingency or pivot table

cases

counts

pivot table / contingency table

##        Favorite
## Age     bubble gum coffee
##   old            2      4
##   young          7      1

Conversions

http://www.cookbook-r.com/Manipulating_data/Converting_between_data_frames_and_contingency_tables/#counts-to-contingency-table

–> MosaicCoding.R

Likert data

• strongly agree
• agree
• don’t know
• disagree
• strongly disagree

Gender equality survey (Pew Research Center)

Stacked Bar Chart

Source: https://blog.datawrapper.de/divergingbars/

Diverging Stacked Bar Chart

Source: https://blog.datawrapper.de/divergingbars/

Diverging Stacked Bar Chart w/ Separate Neutrals

Source: https://blog.datawrapper.de/divergingbars/

Diverging stacked bar chart

Diverging stacked bar chart (neutrals removed)

Political Interests

Source: “Perceived losses of scientific integrity under the Trump administration: A survey of federal scientists”

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231929

Diverging stacked bar chart

HH package

Additional mosaic plot parameters: abbreviate labels

(Note: these examples are designed to highlight one parameter at a time. They should not be taken as complete examples as they do not follow all best practices.)

Abbreviate labels, one setting per variable in order of variable splits

labeling = labeling_border(abbreviate_labs = c(FALSE, 3, 6))

See ?vcd::labelings

Additional mosaic plot parameters: adjust spacing

Change spacing between factor levels, one setting per variable in order of variable splits

spacing = spacing_dimequal(c(.5, .1, 0))

See ?vcd::spacings

Additional mosaic plot parameters: adjust variable names

set_varnames inside labeling function

labeling = labeling_border(set_varnames = c(...))

Additional mosaic plot parameters: rotate labels

Change angle of displayed factor levels, one setting per side, starting with top (top, right, bottom, left)

rot_labels = c(0, 0, 0, 0)

(If labeling = is included, rot_labels should be inside the labeling function, for example:

labeling = labeling_border(rot_labels = c(0, 0, 0, 0), ...)